Abstract
This work addresses the contemporary challenges in computing, caused by the stagnation of Moore's Law and Dennard scaling. The shift towards heterogeneous architectures necessitates innovative compilation strategies, prompting initiatives like the Multi-Level Intermediate Representation (MLIR) project, where progressive code lowering can be achieved through the use of dialects. Our work focuses on developing an MLIR dialect capable of representing streaming data accesses to memory, and Single Instruction Multiple Data (SIMD) vector operations. We also propose our own Structured Representation Language (SRL), a Design Specific Language (DSL) to serve as a precursor into the MLIR layer and subsequent inter-operation between new and existing dialects. The SRL exposes the streaming and vector computational concepts to a higher-level, and serves as intermediate step to supporting code generation containing our proposed dialect from arbitrary input code, which we leave as future work. This paper presents the syntaxes of the SRL DSL and of the dialect, and illustrates how we aim to employ them to target both General-Purpose Processors (GPPs) with SIMD co-processors and custom hardware options such as Field-Programmable Gate Arrayss (FPGAs) and Coarse-Grained Re-configurable Arrays (CGRAs).

Abstract
This work presents an implementation of a reference optical cavity based on parasitic cavities on a low coherence interferometric system. This method allows a maximization of the number of sensors to be implemented without occupying additional reading channels.

Abstract
Featured Application The presented solution can be applied to simplify and hasten the development of gamified programming exercises conforming to the Framework for Gamified Programming Education (FGPE) standard.Abstract Skilled programmers are in high demand, and a critical obstacle to satisfying this demand is the difficulty of acquiring programming skills. This issue can be addressed with automated assessment, which gives fast feedback to students trying to code, and gamification, which motivates them to intensify their learning efforts. Although some collections of gamified programming exercises are available, producing new ones is very demanding. This paper presents GAMAI, an AI-powered exercise gamifier, enriching the Framework for Gamified Programming Education (FGPE) ecosystem. Leveraging large language models, GAMAI enables teachers to effortlessly apply storytelling to describe a gamified scenario, as GAMAI decorates natural language text with the sentences needed by OpenAI APIs to contextualize the prompt. Once a gamified scenario has been generated, GAMAI automatically produces exercise files in a FGPE-compatible format. According to the presented evaluation results, most gamified exercises generated with AI support were ready to be used, with no or minimum human effort, and were positively assessed by students. The usability of the software was also assessed as high by the users. Our research paves the way for a more efficient and interactive approach to programming education, leveraging the capabilities of advanced language models in conjunction with gamification principles.

Abstract

Abstract
Omnichannel retailers are reinventing stores to meet the growing demand of the online channel. Several retailers now use stores as supporting distribution centers to offer quicker Buy-Online-Pickup-In-Store (BOPS) and Ship-From-Store (SFS) services. They resort to in-store picking to serve online orders using existing assets. However, in-store picking operations require picker carts traveling through store aisles, competing for store space, and possibly harming the offline customer experience. To learn picking policies that acknowledge interactions between pickers and offline customers, we formalize a new problem called Dynamic In-store Picker Routing Problem (diPRP). This problem considers a picker that tries to pick online orders (seeking) while minimizing customer encounters (hiding) - preserving the offline customer experience. We model the problem as a Markov Decision Process (MDP) and solve it using a hybrid solution approach comprising mathematical programming and reinforcement learning components. Computational experiments on synthetic instances suggest that the algorithm converges to efficient policies. We apply our solution approach in the context of a large European retailer to assess the proposed policies regarding the number of orders picked and customers encountered. The learned policies are also tested in six different retail settings, demonstrating the flexibility of the proposed approach. Our work suggests that retailers should be able to scale the in-store picking of online orders without jeopardizing the experience of offline customers. The policies learned using the proposed solution approach reduced the number of customer encounters by up to 50%, compared to policies solely focused on picking orders. Thus, to pursue omnichannel strategies that adequately trade-off operational efficiency and customer experience, retailers cannot rely on actual simplistic picking strategies, such as choosing the shortest possible route.

Abstract
The Mid-infrared ELT Imager and Spectrograph (METIS) is a first-generation instrument for the Extremely Large Telescope (ELT), Europe's next-generation 39 m ground-based telescope for optical and infrared wavelengths, which is currently under construction at the European Southern Observatory (ESO) site at Cerro Armazones in Chile. METIS will offer diffraction-limited imaging, low- and medium-resolution slit spectroscopy, and coronagraphy for high-contrast imaging between 3 and 13 microns, as well as high-resolution integral field spectroscopy between 3 and 5 microns. The main METIS science goals are the detection and characterisation of exoplanets, the investigation of proto-planetary disks, and the formation of planets. The Single-Conjugate Adaptive Optics (SCAO) system corrects atmospheric distortions and is thus essential for diffraction-limited observations with METIS. SCAO will be used for all observing modes, with high-contrast imaging imposing the most demanding requirements on its performance. The Final Design Review (FDR) of METIS took place in the fall of 2022; the development of the instrument, including its SCAO system, has since entered the Manufacturing, Assembly, Integration and Testing (MAIT) phase. Numerous challenging aspects of an ELT Adaptive Optics (AO) system are addressed in the mature designs for the SCAO control system and the SCAO hardware module: the complex interaction with the telescope entities that participate in the AO control, wavefront reconstruction with a fragmented and moving pupil, secondary control tasks to deal with differential image motion, non-common path aberrations and mis-registration. A K-band pyramid wavefront sensor and a GPU-based Real-Time Computer (RTC), tailored to the needs of METIS at the ELT, are core components. This current paper serves as a natural sequel to our previous work presented in Hippler et al. (2018). It reflects all the updates that were implemented between the Preliminary Design Review (PDR) and FDR, and includes updated performance estimations in terms of several key performance indicators, including achieved contrast curves. We outline all important design decisions that were taken, and present the major challenges we faced and the main analyses carried out to arrive at these decisions and eventually the final design. We also elaborate on our testing and verification strategy, and, last not least, comprehensively present the full design, hardware and software in this paper to provide a single source of reference which will remain valid at least until commissioning.